Model adaptive apparatus for performing adaptation of a model used in pattern recognition considering recentness of a received pattern data

ABSTRACT

In order to improve recognition performance, a no-speech sound model correction section performs an adaptation of a no-speech sound model which is a sound model representing a no-speech state on the basis of input data observed in an interval immediately before a speech recognition interval for the object of speech recognition and the degree of freshness representing the recentness of the input data.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a model adaptive apparatus and a modeladaptive method, a recording medium, and a pattern recognitionapparatus. More particularly, the present invention relates to a modeladaptive apparatus and a model adaptive method, a recording medium, anda pattern recognition apparatus, which are suitable for use in a case inwhich, for example, speech recognition is performed.

2. Description of the Related Art

There have hitherto been known methods of recognizing words which arespoken in a noisy environment. Typical methods thereof are a PMC(Parallel Model Combination) method, an SS/NSS (SpectralSubtraction/Nonlinear Spectral Subtraction) method, an SFE (StochasticFeature Extraction) method, etc.

The PMC method has satisfactory recognition performance becauseinformation on environmental noise is taken directly into a sound model,but calculation costs are high (since high-level computations arenecessary, the apparatus is large, processing takes a long time, etc.).In the SS/NSS method, at a stage in which features of speech data areextracted, environmental noise is removed. Therefore, the SS/NSS methodhas a lower calculation cost than that of the PMC method and is widelyused at the present time. In the SFE method, in a manner similar to theSS/NSS method, at a stage in which features of a speech signalcontaining environmental noise are extracted, the environmental noise isremoved, and as features, those represented by a probabilitydistribution are extracted. The SFE method, as described above, differsfrom the SS/NSS method and the PMC method in which the features ofspeech are extracted as a point on the feature space, in that thefeatures of speech are extracted as a distribution in the feature space.

In each of the above-described methods, after the extraction of thefeatures of speech, it is determined which one of the sound modelscorresponding to plural words, which are registered in advance, thefeatures match best, and the word corresponding to the sound model whichmatches best is output as a recognition result.

The details of the SFE method are described in Japanese UnexaminedPatent Application Publication No. 11-133992 (Japanese PatentApplication No. 9-300979), etc., which was previously submitted by theapplicant of this application. Furthermore, the details of theperformance comparisons, etc., among the PMC method, the SS/NSS method,and the SFE method are described in, for example, “H. Pao, H. Honda, K.Minamino, M. Omote, H. Ogawa and N. Iwahashi, Stochastic FeatureExtraction for Improving Noise Robustness in Speech Recognition,Proceedings of the 8th Sony Research Forum, SRF98-234, pp. 9-14, October1998”; “N. Iwahashi, H. Pao, H. Honda, K. Minamino, and M. Omote,Stochastic Features for Noise Robust in Speech Recognition, ICASSP'98Proceedings, pp. 633-636, May 1998”; “N. Iwahashi, H. Pao (presenter),H. Honda, K. Minamino and M. Omote, Noise Robust Speech RecognitionUsing Stochastic Representation of Features, ASJ'98-Spring Proceedings,pp. 91-92, March 1998”; “N. Iwahashi, H. Pao, H. Honda, K. Minamino andM. Omote, Stochastic Representation of Features for Noise Robust SpeechRecognition, Technical Report of IEICE, pp. 19-24, SP97-97 (1998-01);etc.

In the above-described SFE method, etc., environmental noise is nottaken into account directly at the stage of speech recognition, that is,information of environmental noise is not input directly into ano-speech sound model, causing a problem of inferior recognitionperformance to occur.

Furthermore, due to the fact that information on environmental noise isnot taken directly into a no-speech sound model, there is anotherproblem in that recognition performance is decreased as the time fromthe start of the speech recognition until the start of speech productionis increased.

SUMMARY OF THE INVENTION

The present invention has been achieved in view of such circumstances.An object of the present invention is to prevent a decrease inrecognition performance as the time from the start of speech recognitionuntil the start of speech production is increased by correcting ano-speech sound model by using environmental noise information.

To achieve the above-mentioned object, in a first aspect, the presentinvention provides a model adaptive apparatus comprising modeladaptation means for performing an adaptation of a predetermined modelused in pattern recognition on the basis of extracted data in apredetermined interval and the degree of freshness representing therecentness of the extracted data.

The pattern recognition may be performed based on a feature distributionin a feature space of input data.

The model adaptation means may perform an adaptation of thepredetermined model by using, as an degree of freshness, a function inwhich the value changes in such a manner as to correspond to thetime-related position of the extracted data in the predeterminedinterval.

The function may be a monotonically increasing function which increasesas time elapses.

The function may be a linear or nonlinear function.

The function may take discrete values or continuous values.

The function may be a second-order function, a third-order function, ora higher-order function.

The function may be a logarithmic function.

The input data may be speech data.

The predetermined model may be a sound model representing noise in aninterval which is not a speech segment.

Data extraction means may optionally comprise:

-   framing means having an input for receiving a source of speech    and/or environmental noise and for producing in response data    frames;-   noise observation interval extraction means for extracting a noise    vector for a number (m) of frames in a noise observation interval;-   feature extraction means responsive to the noise vector (a) and to    an observation vector in a speech recognition interval to produce a    feature vector (y); and-   no-speech sound model correction means responsive to the noise    vector.

In an embodiment, the apparatus may optionally also comprise:

-   power spectrum analysis means for receiving the extracted data;-   noise characteristic calculation means responsive to environmental    noise; and-   feature distribution parameter calculation means for producing a    feature distribution parameter in response to the power spectrum    analysis means and the noise characteristic calculation means.

The apparatus of the above embodiment may optionally further comprise:

-   a plurality of identification function computation means of which    one at least receives a no-speech model, the means receiving the    feature distribution parameter and producing in response a    respective identification function; and-   determination means responsive to the identification functions to    produce a recognition result on the basis of a closest match.

The apparatus may optionally comprise:

-   feature extraction means for extracting the features of the input    data;-   storage means for storing a predetermined number of models into    which the input data is to be classified; and-   classification means for classifying the features of the input data,    corresponding to a predetermined model, which is observed in a    predetermined interval, and for outputting the data as extracted    data.

In a second aspect, the present invention provides a model adaptivemethod comprising a model adaptation step of performing an adaptation ofa predetermined model on the basis of the extracted data in apredetermined interval and the degree of freshness representing therecentness of the extracted data.

In a third aspect, the present invention provides a recording mediumhaving recorded therein a program comprising a model adaptation step ofperforming an adaptation of a predetermined model on the basis ofextracted data in a predetermined interval and the degree of freshnessrepresenting the recentness of the extracted data.

In a fourth aspect, the present invention provides a pattern recognitionapparatus comprising model adaptation means for performing an adaptationof a predetermined model on the basis of extracted data in apredetermined interval and the degree of freshness representing therecentness of the extracted data.

In the model adaptive apparatus and the model adaptive method, therecording medium, and the pattern recognition apparatus of the presentinvention, an adaptation of a predetermined model is performed based onextracted data in a predetermined interval and the degree of freshnessrepresenting the recentness of the extracted data.

The above and further objects, aspects and novel features of theinvention will become more fully apparent from the following detaileddescription when read in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example of the construction of aspeech recognition apparatus according to the present invention.

FIG. 2 is a diagram illustrating the operation of a noise observationinterval extraction section 3 of FIG. 1.

FIG. 3 is a block diagram showing a detailed example of the constructionof a feature extraction section 5 of FIG. 1.

FIG. 4 is a block diagram showing a detailed example of the constructionof a speech recognition section 6 of FIG. 1.

FIG. 5 is a diagram showing an HMM (Hidden Markov Model).

FIG. 6 is a diagram showing simulation results.

FIG. 7 is a diagram showing a normal distribution of a no-speech soundmodel.

FIG. 8 is a block diagram showing an example of the construction of ano-speech sound model correction section 7 of FIG. 1.

FIG. 9 is a diagram showing a state in which a discrete value isconverted into a continuous value.

FIG. 10 is a diagram showing a general freshness function F(x).

FIG. 11 is a diagram showing a first example of the freshness functionF(x).

FIG. 12 is a diagram showing a second example of the freshness functionF(x).

FIG. 13 is a diagram showing a third example of the freshness functionF(x).

FIG. 14 is a diagram showing a fourth example of the freshness functionF(x).

FIG. 15 is a diagram showing a fifth example of the freshness functionF(x).

FIG. 16 is a diagram showing a sixth example of the freshness functionF(x).

FIG. 17 is a block diagram showing an example of the construction of anembodiment of a computer according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows an example of the construction of an embodiment of a speechrecognition apparatus according to the present invention. In this speechrecognition apparatus, a microphone 1 collects produced speech which isthe object for recognition, together with environmental noise, andoutputs it to a framing section 2. The framing section 2 extracts speechdata input from the microphone 1 at a predetermined time interval (forexample, 10 milliseconds), and outputs the extracted data as data of oneframe. The speech data in units of one frame, which is output by theframing section 2, is supplied, as an observation vector “a” in whicheach of the speech data in a time series which form that frame is acomponent, to a noise observation interval extraction section 3 and to afeature extraction section 5. Hereinafter, where appropriate, anobservation vector which is speech data of a t-th frame is denoted asa(t).

The noise observation interval extraction section 3 buffers the speechdata in frame units, which is input from the framing section 2, by anamount of a predetermined time (by an amount of M or more frames),extracts an observation vector “a” for M frames in a noise observationinterval Tn which is from a timing t_(b) at which a speech productionswitch 4 is turned on to a timing ta which is previous by an amount of Mframes, and outputs it to the feature extraction section 5 and ano-speech sound model correction section 7.

The speech production switch 4 is turned on by a user when the userstarts to produce speech and is turned off when the speech production isterminated. Therefore, the produced speech is not contained in thespeech data before timing t_(b) (noise observation interval Tn) at whichthe speech production switch 4 is turned on, and only environmentalnoise is present. Furthermore, the interval from the timing t_(b) atwhich the speech production switch 4 is turned on to a timing t_(d) atwhich the speech production switch 4 is turned off is a speechrecognition interval, and the speech data in that speech recognitioninterval is an object for speech recognition.

The feature extraction section 5 removes the environmental noisecomponents from the observation vector “a” in the speech recognitioninterval after timing t_(b), which is input from the framing section 2,on the basis of the speech data in which only the environmental noise inthe noise observation interval Tn, which is input from the noiseobservation interval extraction section 3, is present, and extracts thefeatures. That is, the feature extraction section 5 performs a Fouriertransform on, for example, the true (the environmental noise is freeremoved) speech data as the observation vector “a” in order to determinethe power spectrum thereof, and calculates a feature vector y in whicheach frequency component of the power spectrum is a component. Themethod of calculating the power spectrum is not limited to a methodusing a Fourier transform. That is, in addition, the power spectrum canbe determined, for example, by what is commonly called a filter bankmethod.

In addition, the feature extraction section 5 calculates a parameter(hereinafter referred to as a “feature distribution parameter”) Zrepresenting the distribution in a feature vector space, which isobtained when speech contained in speech data as an observation vector“a” is mapped into a space (the feature vector space) of the features,on the basis of the calculated feature vector y, and supplies it to thespeech recognition section 6.

FIG. 3 shows a detailed example of the construction of the featureextraction section 5 of FIG. 1. In the feature extraction section 5, theobservation vector “a” input from the framing section 2 is supplied to apower spectrum analysis section 11. In the power spectrum analysissection 11, the observation vector “a” is subjected to a Fouriertransform by, for example, an FFT (Fast Fourier Transform) algorithm,thereby the power spectrum of the speech is extracted as a featurevector. Herein, it is assumed that the observation vector “a” as speechdata of one frame is converted into a feature vector (D-dimensionalfeature vector) formed of D components.

Here, a feature vector obtained from an observation vector a(t) of thet-th frame is denoted as y(t). Furthermore, of the feature vector y(t),the spectrum component of the true speech is denoted as x(t), and thespectrum component of environmental noise is denoted as u(t). In thiscase, the spectrum component of the true speech can be expressed basedon the following equation (1):x(t)=y(t)−u(t)  (1)wherein it is assumed that the environmental noise has irregularcharacteristics, and that the speech data as the observation vector a(t)is such that the environmental noise is added to the true speechcomponent.

In the feature extraction section 5, on the other hand, theenvironmental noise while or in the form of the speech data input fromthe noise observation interval extraction section 3 is input to thenoise characteristic calculation section 13. In the noise characteristiccalculation section 13, the characteristics of the environmental noisein the noise observation interval Tn are determined.

More specifically, herein, assuming that the distribution of the powerspectrum u(t) of the environmental noise in the speech recognitioninterval is the same as that of the environmental noise in the noiseobservation interval Tn immediately before that speech recognitioninterval and that the distribution is a normal distribution, in thenoise characteristic calculation section 13, a mean value (mean vector)of the environmental noise and the variance (variance matrix) thereofare determined.

A mean vector μ′ and a variance matrix Σ′ can be determined based on thefollowing equation (2): $\begin{matrix}\begin{matrix}{{\mu^{\prime}(i)} = {\frac{1}{M}{\sum\limits_{t = 1}^{M}\quad{{y(t)}(i)}}}} \\{{\sum^{\prime}\quad\left( {i,j} \right)} = {\frac{1}{M}{\sum\limits_{t = 1}^{M}\quad{\left( {{{y(t)}(i)} - {\mu^{\prime}(i)}} \right)\left( {{{y(t)}(j)} - {\mu^{\prime}(j)}} \right)}}}}\end{matrix} & (2)\end{matrix}$where the mean vector μ′(i) represents the i-th component of the meanvector μ′(i=1, 2, . . . , D), y(t)(i) represents the i-th component ofthe feature vector of the t-th frame, and Σ′(i, j) represents thecomponent of the i-th row and the j-th column of the variance matrixΣ′(j=1, 2, . . . , D).

Here, in order to reduce the number of calculations, regarding theenvironmental noise, it is assumed that the components of the featurevector y are not in correlation with each other. In this case, as shownin the following equation, the variance matrix Σ′ is 0 except for thediagonal components.Σ′(i,j)=0,i≠j  (3)

In the noise characteristic calculation section 13, in a manner asdescribed above, the mean vector μ′ and the variance matrix Σ′, whichdefine the normal distribution, as the characteristics of theenvironmental noise, are determined, and these are supplied to thefeature distribution parameter calculation section 12.

On the other hand, the output of the power spectrum analysis section 11,that is, the feature vector y of the produced speech containingenvironmental noise, is supplied to the feature distribution parametercalculation section 12. In the feature distribution parametercalculation section 12, a feature distribution parameter representingthe distribution (distribution of estimated values) of the powerspectrum of the true speech is calculated based on the feature vector yfrom the power spectrum analysis section 11 and the characteristics ofthe environmental noise from the noise characteristic calculationsection 13.

More specifically, in the feature distribution parameter calculationsection 12, assuming that the distribution of the power spectrum of thetrue speech is a normal distribution, the mean vector ξ thereof and thevariance matrix Ψ thereof are calculated as feature distributionparameters based on the following equations (4) to (7): $\begin{matrix}\begin{matrix}{{{\xi(t)}(i)} = {E\left\lbrack {{x(t)}(i)} \right\rbrack}} \\{= {E\left\lbrack {{{y(t)}(i)} - {{u(t)}(i)}} \right\rbrack}} \\{= {\int_{0}^{{y{(t)}}{(i)}}{\left( {{{y(t)}(i)} - {{u(t)}(i)}} \right)\frac{p\left( {{u(t)}(i)} \right)}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}\quad{\mathbb{d}{u(t)}}(i)}}} \\{= \frac{{{y(t)}(i){\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}} - \quad{\int_{0}^{{y{(t)}}{(i)}}{{u(t)}(i){P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}} \\{= {{y(t)}(i){\int_{0}^{{y{(t)}}{(i)}}\frac{{u(t)}(i){P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}}}}\end{matrix} & (4)\end{matrix}$When i=j,

-   Ψ(t)(i,j)=V[x(t)(i)]    -   =E[(x(t)(i))²]−(E[x(t)(i)])²    -   (=E[(x(t)(i))²]−(ξ(t)(i))²)        When i≠j,        Ψ(t)(i,j)=0  (5)        $\begin{matrix}        {\begin{matrix}        {E\left\lbrack {\left( {{x(t)}(i)} \right)^{2} = {E\left\lbrack \left( {{{y(t)}(i)} - {{u(t)}(i)}} \right)^{2} \right\rbrack}} \right.} \\        {= {\int_{0}^{{y{(t)}}{(i)}}{\left( {{{y(t)}(i)} - {{u(t)}(i)}} \right)^{2}\frac{p\left( {{u(t)}(i)} \right)}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}\quad{\mathbb{d}{u(t)}}(i)}}} \\        {= {\frac{1}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}} \times}} \\        {\left\{ {{\left( {{y(t)}(i)} \right)^{2}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}} -} \right.} \\        {{2{y(t)}(i){\int_{0}^{{y{(t)}}{(i)}}{{u(t)}(i){P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}} +} \\        {\left. {\int_{0}^{{y{(t)}}{(i)}}{\left( {{u(t)}(i)} \right)^{2}{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}} \right\}} \\        {= {\left( {{y(t)}(i)} \right)^{2} - {2{y(t)}(i){\int_{0}^{{y{(t)}}{(i)}}\frac{{u(t)}(i){P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}}} +}} \\        {\frac{\int_{0}^{{y{(t)}}{(i)}}{\left( {{u(t)}(i)} \right)^{2}{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}{\int_{0}^{{y{(t)}}{(i)}}{{P\left( {{u(t)}(i)} \right)}\quad{\mathbb{d}{u(t)}}(i)}}}        \end{matrix}} & (6) \\        {{P\left( {{u(t)}(i)} \right)} = {\frac{1}{\sqrt{2\pi\quad{\sum^{\prime}\left( {i,i} \right)}}}e^{\frac{1}{2{\sum\limits^{\prime}\quad{({i,i})}}}{({{{u{(t)}}{(i)}} - {\mu^{\prime}{(i)}}})}^{2}}}} & (7)        \end{matrix}$        where ξ(t)(i) represents the i-th component of the mean vector        ξ(t) in the t-th frame, E[] means a mean value within [],        x(t)(i) represents the i-th component of the power spectrum x(t)        of the true speech in the t-th frame, u(t)(i) represents the        i-th component of the power spectrum of the environmental noise        in the t-th frame, and P(u(t)(i)) represents the probability        that the i-th component of the power spectrum of the        environmental noise in the t-th frame is u(t)(i). Herein, since        a normal distribution is assumed as the distribution of the        environmental noise, P(u(t)(i)) can be expressed as shown in        equation (7).

Furthermore, Ψ(t)(i, j) represents the component of the i-th row and thej-th column of the variance Ψ(t) in the t-th frame. In addition, V[]represents the variance within [].

In the feature distribution parameter calculation section 12, in amanner as described above, for each frame, the mean vector ξ and thevariance matrix Ψ are determined as the feature distribution parametersrepresenting the distribution (herein, the distribution in a case wherethe distribution in the feature vector space of the true speech isassumed to be a normal distribution) in the feature vector space of thetrue speech.

Thereafter, the feature distribution parameters determined in each frameof the speech recognition interval are output to the speech recognitionsection 6. That is, if the speech recognition interval is T frames andthe feature distribution parameter determined in each of the T frames isdenoted as z(t)={ξ(t), Ψ(t)} (t=1, 2, . . . , T), the featuredistribution parameter calculation section 12 supplies the featuredistribution parameter (sequence) Z={z(1), z(2), . . . , z(T)} to thespeech recognition section 6.

Referring again to FIG. 1, the speech recognition section 6 classifiesthe feature distribution parameter Z input from the feature extractionsection 5 into one of a predetermined number K of sound models and oneno-speech sound model and outputs the classified result as therecognition result of the input speech. That is, the speech recognitionsection 6 has stored therein, for example, an identification function(function for identifying whether the feature parameter Z is classifiedinto a no-speech sound model) corresponding to a no-speech segment, andidentification functions (functions for identifying whether the featureparameter Z is classified into any one of the sound models)corresponding to each of the predetermined number K of words, andcalculates the value of the identification function of each sound modelby using the feature distribution parameter Z from the featureextraction section 5 as an augment. Then, a sound model (word or nospeech (noise)) in which the function value (what is commonly called ascore) thereof is output as a recognition result.

FIG. 4 shows a detailed example of the construction of the speechrecognition section 6 of FIG. 1. The feature distribution parameter Zinput from the feature distribution parameter calculation section 12 ofthe feature extraction section 5 is supplied to identification functioncomputation sections 21-l and 21-k, and an identification functioncomputation section 21-s. The identification function computationsection 21-k (k=1, 2, . . . , K) has stored therein an identificationfunction G_(k)(Z) for identifying a word corresponding to the k-th soundmodel of K sound models, and computes the identification functionG_(k)(Z) by using the feature distribution parameter Z from the featureextraction section 5 as an augment. The identification functioncomputation section 21-s has stored therein an identification functionG_(s)(Z) for identifying a no-speech segment corresponding to theno-speech sound model, and computes the identification function G_(s)(Z)by using the feature distribution parameter Z from the featureextraction section 5 as an augment.

In the speech recognition section 6, identification (recognition) of aword or no speech as a class is performed by using, for example, an HMM(Hidden Markov Model) method.

The HMM method will now be described with reference to FIG. 5. In FIG.5, the HMM has H states q_(l) to q_(H), and for the state transition,only the transition to oneself and the transition to the state adjacentto the right are permitted. Furthermore, the initial state is set to bethe leftmost state q_(l), the final state is set to be the rightmoststate q_(H), and the state transition from the final state q_(H) isprohibited. In a manner as described above, a model in which there is notransition to the state to the left of oneself is called a left-to-rightmodel, and in the speech recognition, generally, a left-to-right modelis used.

If a model for identifying a k class of the HMM is referred to as ak-class model, the k-class model is defined by, for example, aprobability (initial state probability) π_(k)(q_(h)) in which the modelis initially in a state q_(h), a probability (transition probability)a_(k)(q_(i), q_(j)) in which the model is in a state q_(i) in a time(frame) t and transitions to a state q_(j) at the next time t+1, and aprobability (output probability) b_(k)(q_(i)) in which the state q_(i)outputs a feature vector O when the state transition occurs from thestate q_(i) (h=1, 2, . . . , H).

In a case where a feature vector sequence O₁, O₂, . . . is given, forexample, the class of a model in which the probability (observationprobability) at which such a feature vector sequence is observed ishighest is assumed to be a recognition result of the feature vectorsequence.

Herein, this observation probability is determined by the identificationfunction G_(k)(Z). That is, the identification function G_(k)(Z) isgiven based on the following equation (8) by assuming that, in theoptimum state sequence (the manner in which the optimum state transitionoccurs) with respect to the feature distribution parameter (sequence)z={z₁, Z₂, . . . ,z_(T)}, the identification function G_(k)(Z)determines the probability at which such a feature distributionparameter (sequence) Z={z₁, z₂, . . . , z_(T)} is observed:_(k)(Z)=max π_(k)(q₁)·b_(k)(q₁)(z₁)·a_(k)(q₁ , q ₂)·b_(k)(q₂)(z₂)q₁, q₂, . . . , q _(T). . . a_(k)(q_(Y)-1, q _(T))·b_(k)(q_(T))(Z_(T))  (8)where b_(k)′(q_(i))(z_(j)) represents the output probability when theoutput is a distribution represented by z_(j). For the outputprobability b_(k)(s)(O_(t)) which is a probability at which each featurevector is output during a state transition, herein, a normaldistribution function is used by assuming that there is no correlationamong the components in the feature vector space. In this case, when theinput is a distribution represented by z_(t), the output probabilityb_(k)′(s)(z_(t)) can be determined based on the following equation (9)by using a probability density function P_(k) ^(m)(s)(x) which isdefined by the mean vector μ_(k)(s) and the variance matrix Σ_(k)(s),and a probability density function P_(f)(t)(x) representing the featurevector (here, the power spectrum) x of the t-th frame: $\begin{matrix}\begin{matrix}{{{b_{k}^{\prime}(s)}\left( z_{t} \right)} = {\int{{p^{f}(t)}(x){p_{k}^{m}(s)}(x){\mathbb{d}x}}}} \\{= {\prod\limits_{i = 1}^{D}\quad{{P(s)}(i)\left( {\xi\quad(t)(i)\Psi\quad t} \right)\left( \left( {i,i} \right) \right)}}} \\{{k = 1},2,\ldots,{{K:s} = q_{1}},q_{2},\ldots,{{q_{T}:T} = 1},2,\ldots,T}\end{matrix} & (9)\end{matrix}$where the integration interval of the integration in equation (9) is theentirety of the D-dimensional feature vector space (here, the powerspectrum space).

Furthermore, in equation (9), P(s)(i)(ξ(t)(i), Ψ(t)(i, i)) is expressedbased on the following equation (10): $\begin{matrix}{{{P(s)}(i)\left( {{{\xi(t)}(i)},{{\Psi(t)}\left( {i,i} \right)}} \right)} = {\frac{1}{\sqrt{2{{\pi\pi}\left( {{{k(s)}\left( {i,i} \right)} + {{\psi(t)}\left( {i,i} \right)}} \right.}}}e^{- \frac{{({{{\mu_{k}{(s)}}{(i)}} - {\xi\quad{(t)}{(i)}}})}^{2}}{2{({{\sum\limits_{k}\quad{{(s)}{({i,i})}}} + {{\psi{(t)}}{({i,i})}}}}}}}} & (10)\end{matrix}$where μ_(k)(s)(i) represents the i-th component of the mean vectorμ_(k)(s), and Σ_(k)(s)(i, i) represents the component of the i-th rowand the i-th column of the variance matrix Σ_(k)(s). The outputprobability of the k-class model is defined by these components.

The HMM, as described above, is defined by the initial state probabilityπ_(k)(q_(h)), the transition probability a_(k)(q_(i), q_(j)), and theoutput probability b_(k)(q_(i))(O). These probabilities are determinedin advance by calculating a feature vector from the speech data forlearning and by using the feature vector.

Herein, as the HMM, when that shown in FIG. 5 is used, since thetransition always starts from the leftmost state q_(l), only the initialstate probability corresponding to the state q₁ is set to “1”, and allthe initial state probabilities corresponding to the other states areset to “0”. Furthermore, as is clear from equations (9) and (10), ifΨ(t)(i, i) is set to “0”, the output probability matches the outputprobability in a continuous HMM in a case where the variance of thefeature vector is not taken into account.

As a method of learning an HMM, for example, a Baum-Welch's reestimationmethod, etc., is known.

Referring again to FIG. 4, the identification function computationsection 21-k (k=1, 2, . . . , K) has stored therein, with respect to thek-class model, the identification function G_(k)(Z) of equation (8)which is defined by the initial state probability π_(k)(q_(h)) which isdetermined in advance by learning, the transition probabilitya_(k)(q_(i), q_(j)), and the output probability b_(k)(q_(i))(O). Theidentification function computation section 21-k computes theidentification function G_(k)(Z) by using the feature distributionparameter Z from the feature extraction section 5 as an augment, andoutputs the function value (the above-described observation probability)G_(k)(Z) thereof to a determination section 22. The identificationfunction computation section 21-s has stored therein an identificationfunction G_(s)(Z) similar to the identification function G_(k)(Z) ofequation (8), which is determined by the initial state probabilityπ_(s)(q_(h)) supplied from the no-speech sound model correction section7, the transition probability a_(s)(q_(i), q_(j)), and the outputprobability b_(s)(q_(i))(O). The identification function computationsection 21-s computes the identification function G_(s)(Z) by using thefeature distribution parameter Z from the feature extraction section 5as an augment, and outputs the function value (the above-describedobservation probability) G_(s)(Z) thereof to the determination section22.

In the determination section 22, with respect to the function valueG_(k)(Z) (it is assumed herein that it contains the function valueG_(s)(Z)) from each of the identification function computation sections21-1 and 21-k, and the identification function computation section 21-s,for example, by using the determination rule shown in the followingequation (11), the feature distribution parameter Z, that is, the class(sound model) to which the input speech belongs, is identified:$\begin{matrix}{{{C(Z)} = C_{k}},\quad{{{if}\quad{G_{k}(Z)}} = {\max\limits_{i}\left\{ {G_{i}(Z)} \right\}}}} & (11)\end{matrix}$where C(Z) represents the function for performing an identificationoperation (process) for identifying a class to which the featuredistribution parameter Z belongs, and furthermore, max in the right sideof the second equation of equation (11) represents the maximum value ofthe function value G_(i)(Z) (here, i=s, 1, 2, . . . , K) which follows.

When the determination section 22 determines the class on the basis ofequation (11), the determination section 22 outputs the class as arecognition result of the input speech.

Referring again to FIG. 1, the no-speech sound model correction section7 creates the identification function G_(s)(Z) corresponding to theno-speech sound model stored in the speech recognition section 6 on thebasis of the environmental noise as the speech data in the noiseobservation interval Tn, which is input from the noise observationinterval extraction section 3, and supplies it to the speech recognitionsection 6.

Specifically, in the no-speech sound model correction section 7, afeature vector X is observed with respect to each of M frames of thespeech data (environmental noise) in the noise observation interval Tn,which is input from the noise observation interval extraction section 3,and the feature distribution thereof is created.{F ₁(X), F ₂(X), . . . , F _(M)(X)}  (12)The feature distribution {F_(i)(X), i=1, 2, . . . , M} is a probabilitydensity function, and hereinafter is also referred to as a “no-speechfeature distribution PDF”.

Next, the no-speech feature distribution PDF is mapped into aprobability distribution F_(s)(X) corresponding to the no-speech soundmodel on the basis of equation (13).F _(s)(X)=V(F ₁(X), F ₂(X), . . . , F _(M)(X))  (13)where V is a correction function (mapping function) for mapping theno-speech feature distribution PDF{F_(i)(X), i=1, 2, . . . , M} into theno-speech sound model F_(s)(X).

For this mapping, various methods can be conceived by the description ofthe no-speech feature distribution PDF, for example, $\begin{matrix}{{F_{s}(x)} = {\sum\limits_{i = 1}^{M}\quad{{\beta_{i}\left( {{F_{1}(X)},{F_{2}(X)},\quad{\dddot{}}\quad,{F_{M}(X)},M} \right)} \cdot {F_{i}(X)}}}} & (14) \\{= {\sum\limits_{i = 1}^{M}\quad{\beta_{i}{F_{1}(X)}}}} & (15)\end{matrix}$where β_(i)(F₁(X), F₂(X), . . . , F_(M)(X), M) is a weighting functioncorresponding to each no-speech feature distribution and hereinafter isreferred to as “β_(i)”. The weighting function β_(i) satisfies theconditions of the following equation (16): $\begin{matrix}{{\sum\limits_{i = 1}^{M}\quad{\beta_{i}\left( {{F_{1}(X)},{F_{2}(X)},\quad{\dddot{}}\quad,{F_{M}(X)},M} \right)}} = {{\sum\limits_{i = 1}^{M}\quad\beta_{i}} \equiv 1}} & (16)\end{matrix}$

Here, if it is assumed that the probability distribution F_(s)(X) of theno-speech sound model is a normal distribution and that the componentswhich form the feature vector of each frame are not in correlation witheach other, a covariance matrix Σ_(i) of the no-speech featuredistribution PDF{F_(i)(X), i=1, 2, . . . , M} is a diagonal matrix.However, the precondition for this assumption requires that thecovariance matrix of the no-speech sound model also be a diagonalmatrix. Therefore, if the components which form the feature vector ofeach frame are not in correlation with each other, the no-speech featuredistribution PDF{F_(i)(X), i=1, 2, . . . , M} is a normal distributionG(E_(i), Σ_(i)) having a mean and a variance corresponding to eachcomponent. E_(i) is the mean value of F_(i)(X) (hereinafter alsoreferred to as an “expected value”) where appropriate, and Σ_(i) is thecovariance matrix of F_(i)(X).

In addition, if the mean of the no-speech feature distributioncorresponding to M frames of the noise observation interval Tn isdenoted as μ_(i) and the variance thereof is denoted as σ_(i) ², theprobability density function of the no-speech feature distribution canbe expressed by the normal distribution G(μ_(i), σ_(i) ²) (i=1, 2, . . ., M). Based on the above assumption, by using the mean μ_(i) and thevariance σ_(i) ² corresponding to each frame, it is possible to computethe normal distribution G(μ_(s), σ_(s) ²) (corresponding to theabove-described G_(s)(Z)) which approximates the no-speech sound modelF_(s)(X) by various methods described below.

The first method of computing the normal distribution G(μ_(s), σ_(s) ²)of the no-speech sound model is a method in which the no-speech featuredistribution {G(μ_(i), σ_(i) ²) i=1, 2, . . . , M} is used, and as shownin the following equation (17), the mean of all of μ_(i) is the meanvalue μ_(s) of the no-speech sound model, and as shown in the followingequation (18), the mean of all of σ_(i) ² is the variance σ_(s) ² of theno-speech sound model: $\begin{matrix}{\mu_{s} = {\frac{a}{M}{\sum\limits_{i = 1}^{M}\quad\mu_{i}}}} & (17) \\{\sigma_{s}^{2} = {\frac{b}{M}{\sum\limits_{i = 1}^{m}\quad\sigma_{1}^{2}}}} & (18)\end{matrix}$where a and b are coefficients in which the optimum values aredetermined by simulation.

A second method of computing the normal distribution G(μ_(s), σ_(s) ²)of the no-speech sound model is a method in which those of the no-speechfeature distribution {G(μ₁, σ_(i) ²), i=1, 2, . . . , M} having theexpected value μ_(i) are used, and based on the following equations (19)and (20), the mean value μ_(s) of the no-speech sound model, and thevariance σ_(s) ² thereof are computed: $\begin{matrix}{\mu_{s} = {\frac{a}{M} \cdot {\sum\limits_{i = 1}^{M}\quad\mu_{i}}}} & (19) \\{{{\sigma_{s}^{2}{b \cdot \frac{1}{M}}}{{\cdot {\sum\limits_{i = 1}^{M}\mu_{i}^{2}}} - \mu_{s}^{2}}}\quad} & (20)\end{matrix}$where a and b are coefficients in which the optimum values aredetermined by simulation.

A third method of computing the normal distribution G(μ_(s,) σ_(s) ²) ofthe no-speech sound model is a method in which the mean value μ_(s) ofthe no-speech sound model and the variance σ_(s) ² thereof are computedby a combination of the no-speech feature distribution {G(μ_(i), σ_(i)²), i=1, 2, . . . ,M}.

In this method, the probability static of each no-speech featuredistribution G(μ_(i), σ_(i) ²) is denoted as X_(i):{X ₁ , X ₂ , . . . , X _(M)}  (21)

Here, if the probability static of the normal distribution G(μ_(s),σ_(s) ²) of the no-speech sound model is denoted as X_(s), theprobability static X_(s) can be expressed by a linear combination of theprobability static X_(i) and the weighting function β_(i), as shown inthe following equation (22). The weighting function β_(i) satisfies thecondition of equation (16). $\begin{matrix}{X_{s} = {\sum\limits_{i = 1}^{M}\quad{\beta_{i} \cdot X_{1}}}} & (22)\end{matrix}$

The normal distribution G(μ_(s), σ_(s) ²) of the no-speech sound modelcan be expressed as shown in the following equation (23):$\begin{matrix}{{G\left( {\mu_{s}\sigma_{s}^{2}} \right)} = {G\left( {{\sum\limits_{i = 1}^{M}\quad{\beta_{i}\mu_{1}}},{\sum\limits_{i = 1}^{M}\quad{\beta_{i}^{2}\sigma_{i}^{2}}}} \right)}} & (23)\end{matrix}$

In equation (23), the weighting function β_(i) can generally be, forexample, 1/M. In this case, the mean value μ_(s) of equation (23) andthe variance σ_(s) ² thereof are determined by using predeterminedcoefficients, for example, as shown in the following equations.$\begin{matrix}{\mu_{s} = {\frac{a}{M} \cdot {\sum\limits_{i = 1}^{M}\quad\mu_{i}}}} & (24) \\{\sigma_{s}^{2} = {\frac{b}{M^{2}}{\sum\limits_{i = 1}^{M}\quad\sigma_{i}^{2}}}} & (25)\end{matrix}$where a and b are coefficients in which the optimum values aredetermined by simulation.

In a fourth method of computing the normal distribution G(μ_(s), σ_(s)²) of the no-speech sound model, a statistical populationΩ_(i)={f_(i, j)} corresponding to the probability static X_(i) of theno-speech feature distribution {G(μ_(i), σ_(i) ²), i=1, 2, . . . , M} isassumed. Herein, if {N_(i)≡N; i=1, 2, . . . , M} is assumed, the meanvalue μ_(i) can be obtained based on the following equation (26), andthe variance σ_(i) ² can be obtained based on the following equation(28): $\begin{matrix}{\mu_{i} = {\frac{1}{N}{\sum\limits_{j = 1}^{M}\quad f_{i,j}}}} & (26) \\{\sigma_{i}^{2} = {\frac{1}{N}{\sum\limits_{j = 1}^{M}\quad\left( {f_{i,j}^{2} - \mu_{j}^{2}} \right)}}} & (27) \\{= {{\frac{1}{N}{\sum\limits_{j = 1}^{M}\quad f_{i,j}^{2}}}\quad - \mu_{j}^{2}}} & (28)\end{matrix}$

By rearranging equation (28), the relationship of the following equation(29) holds: $\begin{matrix}{{\frac{1}{N}{\sum\limits_{j = 1}^{M}\quad f_{i,j}^{2}}} = {\sigma_{i}^{2} + \mu_{i}^{2}}} & (29)\end{matrix}$

Herein, if the sum Ω of the statistical population,${\Omega = {\bigcup\limits_{i = 1}^{M}\Omega_{t}}},$is taken into account, the following equations (30) and (31) are derivedfrom equation (26), and the following equations (32) to (34) are derivedfrom equation (29): $\begin{matrix}{{\mu_{s} = {\frac{1}{MN}{\sum\limits_{i = 1}^{M}\quad{\sum\limits_{j = 1}^{N}f_{i,j}}}}}\quad} & (30) \\{= {\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad\mu_{i}}}} & (31) \\{\sigma_{s}^{2}\frac{1}{MN}{\sum\limits_{i = 1}^{M}\quad{\sum\limits_{j = 1}^{N}\quad\left( {f_{i,j} - \mu_{s}} \right)^{2}}}} & (32)\end{matrix}$ $\begin{matrix}{= {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad{\sum\limits_{j = 1}^{N}\quad f_{i,j}^{2}}}} - \mu_{s}^{2}}} & (33) \\{= {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad\left( {\sigma_{i}^{2} + \mu_{i}^{2}} \right)}} - \mu_{s}^{2}}} & (34)\end{matrix}$

In practice, equations (31) and (34) are used by multiplyingcoefficients thereto: $\begin{matrix}{\mu_{s} = {\frac{a}{M}{\sum\limits_{i = 1}^{M}\quad\mu_{i}}}} & (35) \\{\sigma_{s}^{2} = {b \cdot \left( {{\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad\left( {\sigma_{i}^{2} + \mu_{1}^{2}} \right)}} - \mu_{s}} \right)^{2}}} & (36)\end{matrix}$where a and b are coefficients in which the optimum values aredetermined by simulation.

Furthermore, as shown in the following equation (37), a coefficient maybe multiplied to only the variance σ_(s) ². $\begin{matrix}{\sigma_{s}^{2} = {{\frac{b}{M}{\sum\limits_{i = 1}^{M}\quad\sigma_{i}^{2}}} + {\frac{1}{M}{\sum\limits_{i = 1}^{M}\quad\mu_{i}^{2}}} - \mu_{s}^{2}}} & (37)\end{matrix}$

Next, the operation of the speech recognition apparatus of FIG. 1 isdescribed.

Speech data (produced speech containing environmental noise for theobject of recognition) collected by the microphone 1 is input to theframing section 2, whereby the speech data is formed into frames, andthe speech data of each frame is supplied, as an observation vector “a”,to the noise observation interval extraction section 3 and the featureextraction section 5 in sequence. In the noise observation intervalextraction section 3, speech data (environmental noise) in the noiseobservation interval Tn before timing t_(b) at which the speechproduction switch 4 is turned on is extracted, and the speech data issupplied to the feature extraction section 5 and the no-speech soundmodel correction section 7.

In the no-speech sound model correction section 7, based on theenvironmental noise as the speech data in the noise observation intervalTn, updating (adaptation) of the no-speech sound model is performed byone of the above-described first to fourth methods, and the model issupplied to the speech recognition section 6. In the speech recognitionsection 6, an identification function corresponding to the no-speechsound model, which is stored up to that time, is updated by theidentification function as the no-speech sound model supplied from theno-speech sound model correction section 7. That is, an adaptation ofthe no-speech sound model is performed.

In the feature extraction section 5, on the other hand, the speech dataas the observation vector “a” from the framing section 2 is subjected tosound analysis in order to determine the feature vector y thereof.Furthermore, in the feature extraction section 5, based on thedetermined feature vector y, a feature distribution parameter Zrepresenting the distribution in the feature vector space is calculatedand is supplied to the speech recognition section 6. In the speechrecognition section 6, by using the feature distribution parameter fromthe feature extraction section 5, the value of the identificationfunction of the sound model corresponding to no speech and each of apredetermined number K of words is computed, and a sound model in whichthe function value thereof is a maximum is output as the recognitionresult of the speech.

As described above, since the speech data as the observation vector “a”is converted into a feature distribution parameter Z representing thedistribution in the feature vector space which is a space of thefeatures thereof, the feature distribution parameter is such that thedistribution characteristics of noise contained in the speech data aretaken into consideration. Furthermore, since the identification functioncorresponding to the no-speech sound model for identifying (recognizing)no speech is updated on the basis of the speech data in the noiseobservation interval Tn immediately before speech is produced, it ispossible to greatly improve the speech recognition rate.

FIG. 6 shows results of an experiment (simulation) in which the changeof the speech recognition rate was measured when the no-speech segmentTs (see FIG. 2) from when the speech production switch 4 is turned onuntil speech is produced is changed.

In FIG. 6, the curve “a” shows results by a conventional method in whicha no-speech sound model is not corrected (an adaptation of the no-speechsound model is not performed), the curve “b” shows results by the firstmethod, the curve “c” shows results by the second method, the curve “d”shows results by the third method, and the curve “e” shows results bythe fourth method.

The conditions of the experiment are as follows. The speech data usedfor recognition is collected within a car traveling on an expressway.The noise observation interval Tn is approximately 0.2 seconds in 20frames. The no-speech segment Ts was set to 0.05, 0.1, 0.2, 0.3, and 0.5seconds. In the extraction of the features of the speech data, analysiswas performed (the features were obtained by MFCC (Mel-FrequencyCepstral Coefficients) analysis) in an MFCC domain. The number of peopleproducing speech for the object of recognition is eight (four males andfour females), and 303 words were spoken by each person. The number ofwords for which recognition was performed was 5000 words of Japanese.The sound model is an HMM, and learning was performed in advance byusing speech data prepared for learning. In the speech recognition, aviterbi search method was used, and the beam width thereof was set to3000.

In the first, second, and fourth methods, the coefficient “a” was set to1.0, and the coefficient “b” was set to 0.1. In the third method, thecoefficient “a” was set to 1.0, and the coefficient “b” was set to 1.0.

As is clear from FIG. 6, in the conventional method (curve “a”), as theno-speech segment Ts is increased, the speech recognition rate isdecreased considerably. In the first to fourth methods (curves “b” to“e”) of the present invention, even if the no-speech segment Ts isincreased, the speech recognition rate is decreased only slightly. Thatis, according to the present invention, even if the no-speech segment Tsis changed, it is possible for the speech recognition rate to bemaintained at a particular level.

In each of the above-described first to fourth methods, the mean valueμ_(s) which defines the normal distribution G(μ_(s), σ_(s) ²) of theno-speech sound model becomes a mean value of the mean value μ_(i) ofthe no-speech feature distribution G(μ_(i), σ_(i) ²). Therefore, forexample, if the mean value of the mean value μ_(i) of the no-speechfeature distribution G(μ_(i), σ_(i) ²) is denoted as μ, and the normaldistributions of the no-speech sound models, determined by the first tofourth methods, are denoted as G_(s1)(μ, σ_(s1) ²), G_(s2)(μ, σ_(s2) ²),G_(s3)(μ, σ_(s3) ²), and G_(s4)(μ, σ_(s4) ²), respectively, these becomedistributions, in which the mean value μ is the center (center ofgravity), in the feature space.

The adaptation of a no-speech sound model by the above-described firstto fourth methods, based on the no-speech feature distribution G(μ_(i),σ_(i) ²), can be defined by the following equation (38) by using amapping V. Hereinafter, where appropriate, G(μ_(i), σ_(i) ²) isdescribed as G_(i), and G(μ_(s), σ_(s) ²) is described as G_(s).G _(s)(′)=V(G ₁ , G ₂ , . . . , G _(i), . . . )  (38)

Furthermore, herein, as the normal distribution G, a normal distributionis assumed, and the normal distribution is defined by a mean value and avariance. Therefore, if the mean value and the variance which define thenormal distribution G are expressed by μ_(s) and σ_(s) ² as describedabove, the definition of equation (38) can also be expressed byequations (39) and (40) by using the mappings V_(μ) and V_(σ2) of themean value and the variance, respectively:(μ_(s) =V _(μ)(G ₁ , G ₂, . . . )  (39)σ_(s) ² =V _(σ2)(G ₁ ,G ₂, . . . )  (40)

In the first to fourth methods expressed by the above-described mappingsV (V_(μ) and V_(σ2)), the no-speech feature distribution G₁, G₂, . . . ,G_(M) in a time series, obtained from each of the M frames in the noiseobservation interval Tn (FIG. 2), is treated equally.

However, the environmental noise in the noise observation interval,strictly speaking, is not the same as the environmental noise in thenoise observation interval Tn immediately before the noise observationinterval, and furthermore, generally, it is estimated that the moredistant from (the start time t_(c) of) the speech recognition interval,the more the environmental noise in the noise observation interval Tndiffers from the environmental noise in the speech recognition interval.

Therefore, the no-speech feature distribution G₁, G₂, . . . , G_(M) in atime series, obtained from each of the M frames in the noise observationinterval Tn (see FIG. 2), should be treated by weighting to those whichare nearer to the speech recognition interval, rather than being treatedequally (those which are more distant from the speech recognitioninterval should be treated without being given a weight). As a result ofthe above, an adaptation (correction and updating) of a no-speech soundmodel, which further improves speech recognition accuracy, becomespossible.

Accordingly, regarding the no-speech feature distribution G₁, G₂, . . ., G_(M) obtained in the noise observation interval Tn, the degree offreshness representing the recentness thereof (here, corresponding tothe recentness to the speech recognition interval) is introduced, and amethod of performing an adaptation of a no-speech sound model by takingthis freshness into account is described below.

FIG. 8 shows an example of the construction of the no-speech sound modelcorrection section 7 of FIG. 1, which performs an adaptation of ano-speech sound model.

A freshness function storage section 31 has stored therein (parameterswhich define) a freshness function which is a function representing thedegree of freshness such as that described above.

A sequence of observation vectors (here, speech data of M frames) asspeech data (noise) in the noise observation interval Tn, output by thenoise observation interval extraction section 3, is input to acorrection section 32. The correction section 32 obtains a no-speechfeature distribution G₁, G₂, . . . , G_(M) from this observation vector,and performs an adaptation of a no-speech sound model on the basis ofthis distribution and the freshness function stored in the freshnessfunction storage section 31.

Herein, the no-speech feature distribution G₁, G₂, . . . , G_(M)contains discrete values observed in each of the M frames in the noiseobservation interval Tn. If the no-speech sound model correction section7 is a system which processes discrete values, the no-speech featuredistribution G₁, G₂, . . . , G_(M), which contains discrete values, canbe used as it is. However, in a case where the no-speech sound modelcorrection section 7 is a system which processes continuous values, forexample, as shown in FIG. 9, it is necessary to convert the no-speechfeature distribution G₁, G₂, . . . , G_(M), which contains discretevalues, into continuous values by a continuous converter, after whichthe values are processed by the no-speech sound model correction section7. As a method of converting discrete values into continuous values, forexample, there is a method of performing an approximation by a splinefunction.

The discrete values are a finite number of observed values, observed atdiscrete times in a particular finite observation interval, and thecontinuous values are an infinite number of observed values, observed atarbitrary times, in a particular finite (or infinite) observationinterval and are expressed by a particular function.

In a case where the no-speech feature distribution used for anadaptation of a no-speech sound model contains discrete values, thefreshness function also becomes a function of discrete values, and in acase where the no-speech feature distribution contains continuousvalues, the freshness function also becomes a function of continuousvalues.

Next, a freshness function, and an adaptation of a no-speech sound modelusing the freshness function are described differently in a case wherethe freshness function contains discrete values and in a case where thefreshness function contains continuous values.

First, a freshness function F(x) can be defined as shown in, forexample, equations (41) to (43) below:F(x)=0 if x∉Ω _(obs)  (41)F(x ₂)≧F(x ₁) if x ₂ ≧x ₁  (42)∫Ω_(obs) F(x)dx=1  (43)where Ω_(obs) represents the observation interval of the no-speechfeature distribution, and in this embodiment, it corresponds to thenoise observation interval Tn.

Based on equation (41), the freshness function F(x) becomes 0 in otherthan the observation interval Ω_(obs). Furthermore, based on equation(42), the freshness function F(x) is a action which increases as timeelapses or which does not change (in the specification, referred to as a“monotonically increasing function”) in the observation intervalΩ_(obs). Therefore, basically, the nearer to the speech recognitioninterval (see FIG. 2), the larger the value of the freshness functionF(x). Furthermore, based on equation (43), the freshness function F(x)is a function in which when an integration is performed over theobservation interval Ω_(obs), the integrated value thereof becomes 1.Based on equations (41) to (43), the freshness function F(x) becomes,for example, such as that shown in FIG. 10.

Herein, in this embodiment, the freshness function F(x) is used as amultiplier to be multiplied to the no-speech feature distribution, aswill be described later. Therefore, the freshness function F(x) acts asa weight with respect to the no-speech feature distribution to which thevalue of the function is multiplied as a multiplier when the value ofthe function is positive or negative. Furthermore, the freshnessfunction F(x) acts so as to invalidate the no-speech featuredistribution to which the value thereof is multiplied as a multiplierwhen the value is 0 so that no influence is exerted on the adaptation ofthe no-speech sound model.

In the correction section 32 of FIG. 8, by using the freshness functionF(x) such as that described above and the no-speech feature distributionG₁, G₂, . . . , G_(M), basically, the no-speech sound model G_(s) afteradaptation can be determined based on equation (44): $\begin{matrix}\begin{matrix}{G_{s} = {V\left( {G_{1},\quad{\dddot{}}\quad,G_{M}} \right)}} \\{= {\sum\limits_{x = 1}^{M\quad}\quad{{F(x)} \cdot G_{x}}}}\end{matrix} & (44)\end{matrix}$

According to equation (44), the no-speech feature distribution which isnearer to the speech recognition interval is treated by weighting, andan adaptation of a no-speech sound model is performed. As a result, itis possible to improve the speech recognition accuracy even more.

Next, a specific example of the freshness function F(x), and anadaptation of a no-speech sound model using it are described. In thefollowing, it is assumed that the observation interval Ω_(obs) of theno-speech feature distribution (in this embodiment, the noiseobservation interval Tn) is an interval in which x is from 0 to x_(M).Furthermore, as the function values of the freshness function F(x), thevalues of only the observation interval Ω_(obs) are considered (as shownin equation (41), since the function values are 0 in other than theobservation interval Ω_(obs), in the following that point is notmentioned).

As the freshness function F(x), for example, a linear function can beused. In a case where continuous values are taken as the functionvalues, the freshness function F(x) is expressed based on, for example,equation (45):F(x)=α·x  (45)

α in equation (45) is a predetermined constant, and this constant αbecomes 2/x_(M) ² on the basis of the definition of the freshnessfunction of equation (43). Therefore, the freshness function F(x) ofequation (45) is expressed based on equation (46): $\begin{matrix}{{F(x)} = {\frac{2}{x_{M}^{2}}x}} & (46)\end{matrix}$

Here, the freshness function F(x) shown in equation (46) is shown inFIG. 11.

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (47): $\begin{matrix}{G_{s} = {\frac{2}{x_{M}^{2}}{\int_{0}^{x_{M}}{{x \cdot {G_{x}\left( {\mu_{x},\sigma_{x}^{2}} \right)}}\quad{\mathbb{d}x}}}}} & (47)\end{matrix}$where G_(x)(μ_(i), σ_(x) ²) represents a no-speech feature distributionat time x, and μ_(i) and σ_(x) ² are the mean value and the variancewhich define the normal distribution representing the no-speech featuredistribution, respectively.

Next, as the freshness function F(x), for example, a linear functionwhich takes discrete values can be used. In this case, the freshnessfunction F(x) is expressed based on, for example, equation (48):F(x)=α·x =1, 2, . . . x _(M)  (48)

α in equation (48) is a predetermined constant, and this constant αbecomes 2/(x_(M)(x_(M)+1)) on the basis of the definition of thefreshness function of equation (43). Therefore, the freshness functionF(x) of equation (48) is expressed based on equation (49):$\begin{matrix}{{F(x)} = \frac{2 \cdot x}{x_{M}\left( {x_{M} + 1} \right)}} & (49)\end{matrix}$

Herein, the freshness function F(x) expressed by equation (49) is shownin FIG. 12.

In this case, a no-speech sound model G_(s) after adaptation isdetermined based on equation (50): $\begin{matrix}{G_{s} = {\sum\limits_{x = 1}^{x_{M}}\quad{\frac{2 \cdot x}{x_{M}\left( {x_{M} + 1} \right)} \cdot G_{x}}}} & (50)\end{matrix}$where G_(x) represents the no-speech feature distribution at a samplepoint (sample time) x.

Next, as the freshness function F(x), for example, a nonlinear function,such as an exponential function, a high-order binomial function, or alogarithmic function, can be used. In a case where as the freshnessfunction F(x), for example, a second-order function as a high-orderfunction which takes continuous values is used, the freshness functionF(x) is expressed based on, for example, equation (51):F(x)={overscore (α)}·x ²  (51)

α in equation (51) is a predetermined constant, and this constant αbecomes 3/x_(M) ³ on the basis of the definition of the freshnessfunction of equation (43). Therefore, the freshness function F(x) ofequation (51) is expressed based on equation (52): $\begin{matrix}{{F(x)} = {\frac{3}{x_{M}^{3}} \cdot x^{2}}} & (52)\end{matrix}$

Herein, the freshness function F(x) expressed by equation (52) is shownin FIG. 13.

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (53): $\begin{matrix}{G_{s} = {\frac{3}{x_{M}^{3}} \cdot {\int_{0}^{x_{M}}{{x^{2} \cdot {G_{x}\left( {\mu_{x},\sigma_{x}^{2}}\quad \right)}}{\mathbb{d}x}}}}} & (53)\end{matrix}$

Next, as the freshness function F(x), for example, a second-orderfunction as a high-order function which takes discrete values can beused. In this case, the freshness function F(x) is expressed based on,for example, equation (54):F(x)=α·x ₂ x=1, 2, . . . , x _(M)  (54)

α in equation (54) is a predetermined constant, and this constant αbecomes 6/(x_(M)(x_(M)+1)(2x_(M)+1)) on the basis of the definition ofthe freshness function of equation (43). Therefore, the freshnessfunction F(x) of equation (54) is expressed based on equation (55):$\begin{matrix}{{F(x)} = \frac{6 \cdot x^{2}}{{x_{M}\left( {x_{M} + 1} \right)}\left( {{2x_{M}} + 1} \right)}} & (55)\end{matrix}$

Herein, the freshness function F(x) expressed by equation (55) is shownin FIG. 14.

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (56): $\begin{matrix}{G_{s} = {\sum\limits_{i = 1}^{x_{M}}\quad{\frac{6 \cdot x^{2}}{{x_{M}\left( {x_{M} + 1} \right)}\left( {{2x_{M}} + 1} \right)} \cdot G_{x}}}} & (56)\end{matrix}$

Next, in a case where as the freshness function F(x), for example, alogarithmic function which takes continuous values is used, thefreshness function F(x) is expressed based on, for example, equation(57):F(X)=α·log (x+1)  (57)

α in equation (57) is a predetermined constant, and this constant αbecomes 1/((x_(M)+1)log(x_(M)+1)−x_(M)) on the basis of the definitionof the freshness function of equation (43). Therefore, the freshnessfunction F(x) of equation (57) is expressed based on equation (58):$\begin{matrix}{{F(x)} = {\frac{1}{{\left( {x_{M} + 1} \right){\log\left( {x_{M} + 1} \right)}} - x_{M}} \cdot {\log\left( {x + 1} \right)}}} & (58)\end{matrix}$

Herein, the freshness function F(x) expressed by equation (58) is shownin FIG. 15.

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (59): $\begin{matrix}{G_{s} = {\frac{1}{{\left( {x_{M} + 1} \right){\log\left( {x_{M} + 1} \right)}} - x_{M}} \cdot {\int_{0}^{XM}{\log\quad{\left( {x + 1} \right) \cdot {G_{x}\left( {\mu_{x},\sigma_{x}^{2}} \right)}}{\mathbb{d}x}}}}} & (59)\end{matrix}$

Next, as the freshness function F(x), for example, a logarithmicfunction which takes discrete values can be used. In this case, thefreshness function F(x) is expressed based on, for example, equation(60):F(x)=α·log (x+1) x=1, 2, . . . , x _(M)  (60)

α in equation (60) is a predetermined constant, and this constant α isdetermined on the basis of the definition of the freshness function ofequation (43). Therefore, the freshness function F(x) of equation (60)is expressed based on equation (61): $\begin{matrix}{{F(x)} = \frac{1}{\log{\prod\limits_{y = 1}^{x_{M}}\quad\left( {y + 1} \right)}}} & (61)\end{matrix}$

Herein, the freshness function F(x) expressed by equation (61) is shownin FIG. 16.

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (62): $\begin{matrix}{G_{s} = {\frac{1}{\log\quad{\prod\limits_{y = 1}^{XM}\left( {y + 1} \right)}} \cdot {\sum\limits_{x = 1}^{XM}{\log\quad{\left( {x + 1} \right) \cdot G_{x}}}}}} & (62)\end{matrix}$

Next, in a case where as the freshness function F(x), for example, ageneral high-order function which takes continuous values is used, thefreshness function F(x) is expressed based on, for example, equation(63):F(x)=α·x ^(p)  (63)

α in equation (63) is a predetermined constant, and the degree of thefreshness function F(x) is determined by p.

The constant α can be determined on the basis of the definition of thefreshness function of equation (43). Therefore, the freshness functionF(x) of equation (63) is expressed based on equation (64):$\begin{matrix}{{F(x)} = {\frac{p + 1}{x_{M}^{p + 1}} \cdot x^{p}}} & (64)\end{matrix}$

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (65): $\begin{matrix}{G_{s} = {\frac{p + 1}{x_{M}^{p + 1}} \cdot {\int_{0}^{XM}{{x^{p} \cdot {G_{x}\left( {\mu_{x},\sigma_{x}^{2}} \right)}}{\mathbb{d}x}}}}} & (65)\end{matrix}$

In equation (64), for example, when p is 1 or 2, the freshness functionF(x) is a linear function or a second-order function which takescontinuous values, and is expressed as shown in equation (46) or (52).

Furthermore, in equation (64), for example, when p is 3, the freshnessfunction F(x) is a third-order function which takes continuous valuesand is expressed as shown in equation (66): $\begin{matrix}{{F(x)} = {\frac{4}{x_{M}^{4}} \cdot x^{3}}} & (66)\end{matrix}$

Furthermore, in equation (64), for example, when p is 4, the freshnessfunction F(x) is a fourth-order function which takes continuous valuesand is expressed as shown in equation (67): $\begin{matrix}{{F(x)} = {\frac{5}{x_{M}^{5}} \cdot x^{4}}} & (67)\end{matrix}$

Next, in a case where as the freshness function F(x), for example, ageneral high-order function which takes discrete values is used, thefreshness function F(x) is expressed based on, for example, equation(68):F(x)=α·x ^(p) x=1, 2, . . . , x _(M)  (68)

α in equation (68) is a predetermined constant, and the order of thefreshness function F(x) is determined by p.

The constant α can be determined on the basis of the definition of thefreshness function of the equation (43). Therefore, the freshnessfunction F(x) of equation (68) is expressed based on equation (69):$\begin{matrix}{{F(x)} = \frac{x^{P}}{\sum\limits_{y = 1}^{XM}y^{p}}} & (69)\end{matrix}$

In this case, the no-speech sound model G_(s) after adaptation isdetermined based on equation (70): $\begin{matrix}{G_{s} = {\frac{1}{\sum\limits_{y = 1}^{XM}y^{p}} \cdot {\sum\limits_{x = 1}^{XM}{\cdot G_{x}}}}} & (70)\end{matrix}$

In equation (69), for example, when p is 1 or 2, the freshness functionF(x) is a linear function or a second-order function which takesdiscrete values, and is expressed as shown in equation (49) or (55).

In addition, in equation (69), for example, when p is 3, the freshnessfunction F(x) is a third-order function which takes discrete values andis expressed as shown in equation (77): $\begin{matrix}{{F(x)} = \frac{4x^{3}}{{x_{M}^{2}\left( {x_{M} + 1} \right)}^{2}}} & (71)\end{matrix}$

Furthermore, in equation (69), for example, when p is 4, the freshnessfunction F(x) is a fourth-order function which takes discrete values andis expressed as shown in equation (72): $\begin{matrix}{{F(x)} = \frac{4x^{3}}{{x_{M}\left( {x_{M} + 1} \right)}\quad\left( {{2x_{M}} + 1} \right)\quad\left( {{3x_{M}^{2}} + {3x_{M}} - 1} \right)}} & (72)\end{matrix}$

The concept of the freshness function F(x) can be applied to theadaptation of a no-speech sound model, and in addition, to adaptation tothe person speaking in a noisy environment and to the adaptation of asound model other than a no-speech sound model. In addition, it is alsopossible to apply the concept of the freshness function F(x) to speechdetection and non-stationary noise detection. Furthermore, also in thefield of sound signal processing, image signal processing, andcommunication, use of the concept of the freshness function F(x) makesit possible to improve robustness against environmental noise and toimprove system performance.

In the foregoing, although a speech recognition apparatus to which thepresent invention is applied has been described, such a speechrecognition apparatus can be applied to, for example, a car navigationapparatus capable of accepting speech input, and other various types ofapparatuses.

In this embodiment, a feature distribution parameter in whichdistribution characteristics of noise are taken into consideration isdetermined. This noise includes, for example, noise from the outside inan environment in which speech is produced, and in addition, includes,for example, characteristics of a communication line in a case wherespeech is recognized which was transmitted via a telephone line or othercommunication lines.

Furthermore, the present invention can also be applied to a case inwhich, in addition to speech recognition, image recognition and otherpattern recognitions are performed.

For instance, the teachings of the invention can also be transposed topattern recognition systems and method in such application as:

-   object identification and sorting, e.g. in robotics computer-aided    assembling, identification of persons or vehicles, etc.;-   document authentification;-   optical handwriting recognition,-   etc.

In addition, although in this embodiment an adaptation of a no-speechsound model is performed by using a no-speech feature distributionrepresented as a distribution in a feature space, the adaptation of ano-speech sound model can also be performed by using features of noiserepresented as a point in a feature space.

Next, the above-described series of processing can be performed byhardware and can also be performed by software. In a case where theseries of processing is performed by software, programs which form thesoftware are installed into a general-purpose computer, etc.

Accordingly, FIG. 17 shows an example of the construction of anembodiment of a computer into which the programs which execute theabove-described series of processing are installed.

The programs may be recorded in advance in a hard disk 105 or a ROM 103as a recording medium contained in the computer.

Alternatively, the programs may be temporarily or permanently stored(recorded) in a removable recording medium 111, such as a floppy disk, aCD-ROM (Compact Disc Read Only Memory), an MO (Magneto-optical) disk aDVD (Digital Versatile Disc), a magnetic disk, or a semiconductormemory. Such a removable recording medium 111 may be provided as what iscommonly called package software.

In addition to being installed into a computer from the removablerecording medium 111 such as that described above, programs may betransferred in a wireless manner from a download site via an artificialsatellite for digital satellite broadcasting or may be transferred bywire to a computer via a network, such as a LAN (Local Area Network) orthe Internet, and in the computer, the programs which are transferred insuch a manner are received by a communication section 108 and areinstalled into the hard disk 105 contained therein.

The computer has a CPU (Central Processing Unit) 102 contained therein.An input/output interface 110 is connected to the CPU 102 via a bus 101.When a command is input as a result of the user operating an inputsection 107 formed of a keyboard, a mouse, etc., via the input/outputinterface 110, the CPU 102 executes a program stored in a ROM (Read OnlyMemory) 103 in accordance with the command. Alternatively, the CPU 102loads a program stored in the hard disk 105, a program which istransferred from a satellite or a network, which is received by thecommunication section 108, and which is installed into the hard disk105, or a program which is read from the removable recording medium 111loaded into a drive 109 and which is installed into the hard disk 105,to a RAM (Random Access Memory) 104, and executes the program. As aresult, the CPU 102 performs processing performed according to theconstructions in the above-described block diagrams. Then, the CPU 102outputs the processing result from a display section 106 formed of anLCD (Liquid Crystal Display), a speaker, etc., for example, via theinput/output interface 110, as required, or transmits the processingresult from the communication section 108, and furthermore, records theprocessing result in the hard disk 105.

Herein, in this specification, processing steps which describe a programfor causing a computer to perform various types of processing need notnecessarily perform processing in a time series along the describedsequence as a flowchart and to contain processing performed in parallelor individually (for example, parallel processing or object-orientedprocessing) as well.

Furthermore, a program may be such that it is processed by one computeror may be such that it is processed in a distributed manner by pluralcomputers. In addition, a program may be such that it is transferred toa remote computer and is executed thereby.

According to the model adaptive apparatus and the model adaptive method,the recording medium, and the pattern recognition apparatus of thepresent invention, an adaptation of a predetermined model is performedbased on extracted data in a predetermined interval and the degree offreshness representing the recentness of the extracted data. Therefore,by performing pattern recognition using the model, it is possible toimprove recognition performance.

Many different embodiments of the present invention may be constructedwithout departing from the spirit and scope of the present invention. Itshould be understood that the present invention is not limited to thespecific embodiment described in this specification. To the contrary,the present invention is intended to cover various modifications andequivalent arrangements within the scope of the invention as hereafterclaimed.

1. A model adaptive apparatus for performing an adaptation of a modelused in pattern recognition which classifies input data in a time seriesinto one of a predetermined number of models, said model adaptiveapparatus comprising: data extraction means for extracting said inputdata, corresponding to a predetermined model, which is observed in apredetermined interval, and for outputting the data as extracted data;and model adaptation means for performing an adaptation of saidpredetermined model on the basis of the extracted data in saidpredetermined interval and the degree of freshness representing therecentness of the extracted data, wherein said model adaptation meansuses, as said freshness, a function in which the value changes in such amanner as to correspond to the time-related position of said extracteddata in said predetermined interval.
 2. A model adaptive apparatusaccording to claim 1, wherein said pattern recognition is performedbased on a feature distribution in a feature space of said input data.3. A model adaptive apparatus according to claim 1, further comprising:feature extraction means fur extracting the features of said input data;storage means for storing a predetermined number of models into whichsaid input data is to be classified; and classification means forclassifying the features of said input data, corresponding to apredetermined model, which is observed in a predetermined interval, andfor outputting the data as extracted data.
 4. A model adaptive apparatusaccording to claim 1, wherein said input data is speech data.
 5. A modeladaptive apparatus according to claim 4, wherein said predeterminedmodel is a sound model representing noise in an interval which is not aspeech segment.
 6. A model adaptive apparatus according to claim 1,wherein said function is a monotonically increasing function whichincreases as time elapses.
 7. A model adaptive apparatus according toclaim 6, wherein said function is a linear or nonlinear function.
 8. Amodel adaptive apparatus according to claim 6, wherein said functiontakes discrete values or continuous values.
 9. A model adaptiveapparatus according to claim 6, wherein said function is a second-orderfunction, a third-order function, or a higher-order function.
 10. Amodel adaptive apparatus according to claim 6, wherein said function isa logarithmic function.
 11. A model adaptive apparatus for performing anadaptation of a model used in pattern recognition which classifies inputdata in a time series into one of a predetermined number of models, saidmodel adaptive apparatus comprising: data extraction means forextracting said input data, corresponding to a predetermined model,which is observed in a predetermined interval, and for outputting thedata as extracted data; model adaptation means for performing anadaptation of said predetermined model on the basis of the extracteddata in said predetermined interval and the degree of freshnessrepresenting the recentness of the extracted data; power spectrumanalysis means for receiving said extracted data; noise characteristiccalculation means responsive to environmental noise; and featuredistribution parameter calculation means for producing a featuredistribution parameter (Z) in response to said power spectrum analysismeans and said noise characteristic calculation means.
 12. A modeladaptive apparatus according claim 11, further comprising: a pluralityof identification function computation means of which one at leastreceives a no-speech model, said means receiving said featuredistribution parameter (Z) and producing in response a respectiveidentification function (G_(s)(Z), G₁(Z)-G_(k)(Z)); and determinationmean responsive to said identification functions to produce arecognition result on the basis of a closest match.
 13. A model adaptiveapparatus for performing an adaptation of a model used in patternrecognition which classifies input data in a time series into one of apredetermined number of models, said model adaptive apparatuscomprising: data extraction means for extracting said input data,corresponding to a predetermined model, which is observed in apredetermined interval, and for outputting the data as extracted data;and model adaptation means for performing an adaptation of saidpredetermined model on the basis of the extracted data in saidpredetermined interval and the degree of freshness representing therecentness of the extracted data. wherein said data extraction meanscomprise: framing means having an input for receiving a source of speechand/or environmental noise and for producing in response data frames;noise observation interval extraction means for extracting a noisevector for a number (M) of flames in a noise observation interval (Tn);feature extraction means responsive to said noise vector (a) and to anobservation vector (a) in a speech recognition interval to produce afeature vector (y); and no-speech sound model correction meansresponsive to said noise vector.
 14. A pattern recognition apparatus forclassifying input data in a time series into one of a predeterminednumber of models, said pattern recognition apparatus comprising: featureextraction means for extracting the features of said input data; storagemeans for storing said predetermined number of models; classificationmeans for classifying the features of said input data into one of saidpredetermined number of models; data extraction means for extractingsaid input data, corresponding to a predetermined model, which isobserved in a predetermined interval, and for outputting the data asextracted data; and model adaptation means for performing an adaptationof said predetermined model on the basis of the extracted data in saidpredetermined interval and the degree of freshness representing therecentness of the extracted data, wherein said model adaptation meansuses, as said freshness, a function in which the value changes in such amanner as to correspond to the time-related position of said extracteddata in said predetermined interval.
 15. A model adaptive method forperforming an adaptation of a model used in pattern recognition whichclassifies input data in a time series into one of a predeterminednumber of models, said model adaptive method comprising: a dataextraction step of extracting said input data, corresponding to apredetermined model, which is observed in a predetermined interval, andof outputting the data as extracted data; and a model adaptation step ofperforming an adaptation of said predetermined model on the basis of theextracted data in said predetermined interval and the degree offreshness representing the recentness of the extracted data, whereinsaid model adaptation step uses, as said freshness, a function in whichthe value changes in such a manner as to correspond to the time-relatedposition of said extracted data in said predetermined interval.
 16. Arecording medium having recorded therein a program for causing acomputer to perform an adaptation of a model used in pattern recognitionwhich classifies input data in a time series into one of a predeterminednumber of models, said program comprising: a data extraction step ofextracting said input data, corresponding to a predetermined model,which is observed in a predetermined interval, and of outputting thedata as extracted data; and a model adaptation step of performing anadaptation of said predetermined model on the basis of the extracteddata in said predetermined interval and the degree of freshnessrepresenting the recentness of the extracted data, wherein said modeladaptation step uses, as said freshness, a function in which the valuechanges in such a manner as to correspond to the time-related positionof said extracted data in said predetermined interval.